Two-step calibration method for three-axis magnetic sensor error based on particle swarm optimization

Sensor Review ◽  
2020 ◽  
Vol 40 (5) ◽  
pp. 577-583
Author(s):  
Yanxia Liu ◽  
Zhikai Hu ◽  
JianJun Fang
Keyword(s):  
Magnetic Field ◽  
Fitness Function ◽  
Particle Swarm ◽  
Calibration Method ◽  
Magnetic Sensors ◽  
Earth’S Magnetic Field ◽  
Swarm Optimization ◽  
Error Matrix ◽  
Content Type ◽  
Earth's Magnetic Field

Purpose The three-axis magnetic sensors are mostly calibrated by scalar method such as ellipsoid fitting and so on, but these methods cannot completely determine the 12 parameters of the error model. A two-stage calibration method based on particle swarm optimization (TSC-PSO) is proposed, which makes full use of the amplitude invariance and direction invariance of Earth’s magnetic field vector. Design/methodology/approach The TSC-PSO designs two-stage fitness function. Stage 1: design a fitness function of the particle swarm by the amplitude invariance of the Earth’s magnetic field to obtain a preliminary error matrix G and the bias error B. Stage 2: further design the fitness function of the particle swarm by the invariance of the Earth’s magnetic field to obtain a rotation matrix R, thereby determining the error matrix uniquely. Findings The proposed TSC-PSO can completely determine 12 unknown parameters in error model and further decrease the maximum fluctuation error of the Earth’s magnetic field amplitude and the absolute error of heading. Practical implications The proposed TSC-PSO provides an effective solution for three-axis magnetic sensor error compensation, which can greatly reduce the price of magnetic sensors and be used in the fields of Earth’s magnetic survey, drilling and Earth’s magnetic integrated navigation. Originality/value The proposed TSC-PSO has significantly improved the magnetic field amplitude and heading accuracy and does not require additional heading reference. In addition, the method is insensitive to noise and initialization conditions, has good robustness and can converge to a global optimum.

Measured accuracy improvement method of velocity and displacement based on adaptive Kalman filter

Sensor Review ◽  
2019 ◽  
Vol 39 (5) ◽  
pp. 708-715
Author(s):  
Xiaobin Xu ◽  
Minzhou Luo ◽  
Zhiying Tan ◽  
Min Zhang ◽  
Hao Yang
Keyword(s):  
Particle Swarm Optimization ◽  
Kalman Filter ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Fitness Function ◽  
Particle Swarm ◽  
Adaptive Kalman Filter ◽  
Swarm Optimization ◽  
Content Type ◽  
Kalman Filter Algorithm

Purpose This paper aims to investigate the effect of unknown noise parameters of Kalman filter on velocity and displacement and to enhance the measured accuracy using adaptive Kalman filter with particle swarm optimization algorithm. Design/methodology/approach A novel method based on adaptive Kalman filter is proposed. Combined with the displacement measurement model, the standard Kalman filtering algorithm is established. The particle swarm optimization algorithm fused with Kalman is used to obtain the optimal noise parameter estimation using different fitness function. Findings The simulations and experimental results show that the adaptive Kalman filter algorithm fused with particle swarm optimization can improve the accuracy of the velocity and displacement. Originality/value The adaptive Kalman filter algorithm fused with particle swarm optimization can serve as a new method for optimal state estimation of moving target.

scholarly journals Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

2020 ◽  
Author(s):  
Mojtaba Karimi ◽  
Edwin Babaians ◽  
Martin Oelsch ◽  
Tamay Aykut ◽  
Eckehard Steinbach
Keyword(s):  
Magnetic Field ◽  
Hall Effect ◽  
Field Measurement ◽  
Magnetic Measurement ◽  
Magnetic Sensors ◽  
Magnetic Field Measurement ◽  
Earth’S Magnetic Field ◽  
Fusion Technique ◽  
Earth's Magnetic Field ◽  
Heading Estimation

Robust attitude and heading estimation with respect to a known reference is an essential component for indoor localization in robotic applications. Affordable Attitude and Heading Reference Systems (AHRS) are typically using 9-axis solid-state MEMS-based sensors. The accuracy of heading estimation on such a system depends on the Earth's magnetic field measurement accuracy. The measurement of the Earth's magnetic field using MEMS-based magnetometer sensors in an indoor environment, however, is strongly affected by external magnetic perturbations. This paper presents a novel approach for robust indoor heading estimation based on skewed-redundant magnetometer fusion. A tetrahedron platform based on Hall-effect magnetic sensors is designed to determine the Earth's magnetic field with the ability to compensate for external magnetic field anomalies. Additionally, a correlation-based fusion technique is introduced for perturbation mitigation using the proposed skewed-redundant configuration. The proposed fusion technique uses a correlation coefficient analysis for determining the distorted axis and extracts the perturbation-free Earth's magnetic field vector from the redundant magnetic measurement. Our experimental results show that the proposed scheme is able to successfully mitigate the anomalies in the magnetic field measurement and estimates the Earth's true magnetic field. Using the proposed platform, we achieve a Root Mean Square Error of 12.74$\degree$ for indoor heading estimation without using an additional gyroscope.

Particle swarm optimization-based optimal real Gabor filter for surface inspection

2019 ◽  
Vol 39 (5) ◽  
pp. 963-972 ◽  
Author(s):  
Hao Wu ◽  
Xiangrong Xu ◽  
Jinbao Chu ◽  
Li Duan ◽  
Paul Siebert
Keyword(s):  
Particle Swarm Optimization ◽  
Standard Deviation ◽  
Metal Surface ◽  
Gabor Filter ◽  
Fitness Function ◽  
Particle Swarm ◽  
Average Energy ◽  
Swarm Optimization ◽  
Content Type ◽  
Filter Model

Purpose The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated. Design/methodology/approach The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area. Findings The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface. Originality/value The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.

scholarly journals Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

2020 ◽  
Author(s):  
Mojtaba Karimi ◽  
Edwin Babaians ◽  
Martin Oelsch ◽  
Tamay Aykut ◽  
Eckehard Steinbach
Keyword(s):  
Magnetic Field ◽  
Hall Effect ◽  
Field Measurement ◽  
Magnetic Measurement ◽  
Magnetic Sensors ◽  
Magnetic Field Measurement ◽  
Earth’S Magnetic Field ◽  
Fusion Technique ◽  
Earth's Magnetic Field ◽  
Heading Estimation

Robust attitude and heading estimation with respect to a known reference is an essential component for indoor localization in robotic applications. Affordable Attitude and Heading Reference Systems (AHRS) are typically using 9-axis solid-state MEMS-based sensors. The accuracy of heading estimation on such a system depends on the Earth's magnetic field measurement accuracy. The measurement of the Earth's magnetic field using MEMS-based magnetometer sensors in an indoor environment, however, is strongly affected by external magnetic perturbations. This paper presents a novel approach for robust indoor heading estimation based on skewed-redundant magnetometer fusion. A tetrahedron platform based on Hall-effect magnetic sensors is designed to determine the Earth's magnetic field with the ability to compensate for external magnetic field anomalies. Additionally, a correlation-based fusion technique is introduced for perturbation mitigation using the proposed skewed-redundant configuration. The proposed fusion technique uses a correlation coefficient analysis for determining the distorted axis and extracts the perturbation-free Earth's magnetic field vector from the redundant magnetic measurement. Our experimental results show that the proposed scheme is able to successfully mitigate the anomalies in the magnetic field measurement and estimates the Earth's true magnetic field. Using the proposed platform, we achieve a Root Mean Square Error of 12.74$\degree$ for indoor heading estimation without using an additional gyroscope.

The Atlas of the Earth's Magnetic Field

2012 ◽  
Author(s):  
A. Soloviev ◽  
A. Khokhlov ◽  
E. Jalkovsky ◽  
A. Berezko ◽  
A. Lebedev ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Earth’S Magnetic Field ◽  
Earth's Magnetic Field

Atlas of Earth's magnetic field

2011 ◽  
Vol 12 (2) ◽  
pp. 1-9
Author(s):  
A. E. Berezko ◽  
A. V. Khokhlov ◽  
A. A. Soloviev ◽  
A. D. Gvishiani ◽  
E. A. Zhalkovsky ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Earth’S Magnetic Field ◽  
Earth's Magnetic Field

scholarly journals Night-time Sferic Propagation at Frequencies Below 10 KHz

1967 ◽  
Vol 20 (1) ◽  
pp. 101 ◽  
Author(s):  
KJW Lynn ◽  
J Crouchley
Keyword(s):  
Magnetic Field ◽  
Geographic Distribution ◽  
Angle Of Incidence ◽  
European Studies ◽  
Earth’S Magnetic Field ◽  
Night Time ◽  
The West ◽  
Earth's Magnetic Field ◽  
Effective Angle

Results of a study at Brisbane of individual night-time sferics of known origin are described. A propagation attenuation minimum was observed in the 3-6 kHz range. The geographic distribution of sferic types was also examined. Apparent propagation asynunetries were observed, since sferics were detected at greater ranges to the west than to the east at 10 kHz, whilst the number of tweek-sferics arising from the east was about four times that arising from the west. Comparison with European studies suggest that these asymmetries are general. These results are then " interpreted in terms of an ionospheric reflection cgefficient which is a function of the effective angle of incidence of the wave on the ionosphere and of orientation with respect to the Earth's magnetic field within the ionosphere.

scholarly journals Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization

2021 ◽  
Vol 13 (13) ◽  
pp. 7152
Author(s):  
Mike Spiliotis ◽  
Alvaro Sordo-Ward ◽  
Luis Garrote
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Flood Routing ◽  
Performance Criteria ◽  
Extension Principle ◽  
Swarm Optimization ◽  
Muskingum Model ◽  
Muskingum Method ◽  
Fuzzy Parameters

The Muskingum method is one of the widely used methods for lumped flood routing in natural rivers. Calibration of its parameters remains an active challenge for the researchers. The task has been mostly addressed by using crisp numbers, but fuzzy seems a reasonable alternative to account for parameter uncertainty. In this work, a fuzzy Muskingum model is proposed where the assessment of the outflow as a fuzzy quantity is based on the crisp linear Muskingum method but with fuzzy parameters as inputs. This calculation can be achieved based on the extension principle of the fuzzy sets and logic. The critical point is the calibration of the proposed fuzzy extension of the Muskingum method. Due to complexity of the model, the particle swarm optimization (PSO) method is used to enable the use of a simulation process for each possible solution that composes the swarm. A weighted sum of several performance criteria is used as the fitness function of the PSO. The function accounts for the inclusive constraints (the property that the data must be included within the produced fuzzy band) and for the magnitude of the fuzzy band, since large uncertainty may render the model non-functional. Four case studies from the references are used to benchmark the proposed method, including smooth, double, and non-smooth data and a complex, real case study that shows the advantages of the approach. The use of fuzzy parameters is closer to the uncertain nature of the problem. The new methodology increases the reliability of the prediction. Furthermore, the produced fuzzy band can include, to a significant degree, the observed data and the output of the existent crisp methodologies even if they include more complex assumptions.

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